Hello World

Sep 22, 2017
2 minute read

I graduated from Michigan State University in May 2017 with a bachelor’s of science in computer science. While I was earning my degree, I enjoyed taking classes covering various topics of computer science and software developement and gaining relevant experience outside of the classroom through internships. I also love math! I wanted to explore other topics in math outside the required calculus courses and took extra classes such as Linear Algebra, Matrix Algrebra, Abstract Algebra / Number Theory, and Statistics. These classes where all great, but how could I leverage what I’ve learned and combine that with my computer science knowledge?

Through my own research, I found that the area of data science utilizes both math and computer science knowledge. For my senior capstone project, I prioritized projects that satisfied these interests. I ended up working on a great project with a great team and corporate sponsor. I had the opportunity to analyze large sets of data, build software from the ground-up, and deliver our results to corporate sponsors, all with a student team. Coming into college, a career as a software engineer was what initially sparked my interest in computer science, but it was the extra math classes and data science-like capstone project that really pivoted my career interests from software engineering to data science.

It’s now early May 2017. I’ve graduated (hooray!), and I’m now back home in Park Ridge, Illinois thinking about what’s next. I fortunately live close to Chicago, a large city with many junior data science position postings. Reading some of these postings at the time, I thought that the skills and knowledge they required were not far off from what I currently had:

Bachelor’s degree in something quantitative? Check

Knowledge and experience with Python? Check

Working with analyzing large data sets and communicating results? Check

How do I get my foot in the door with what I have? There are countless resources online that cover data science and help with building a portfolio, however, thinking about how I learn and retain information best, some sort of outside structure or mentorship would benefit me more. The mentorship and structure of a data science bootcamp sounded like a good fit for me. My own further research led me to Metis.

What made me consider Metis was the quick-paced and organized structure, strong career mentorship, instructors with years of data science and other quantitative experience, the diversity and quality of students they selected, and the type of places the graduates accepted jobs from. After visiting the Chicago cohort location and going through the application process, I was accepted into the fall cohort of 2017. I gladly accepted their offer and could not be more excited for the 12 weeks at Metis!